Synthetic population explained

Synthetic population is artificial population data that fits the distribution of people and their relevant characteristics living in a specified area as according to the demographics from census data.[1] Synthetic populations are often a basis for microsimulation or also agent based models of population behavior.[2] The latter can be used for simulation of disease transmission,[3] traffic[4] and similar.

Synthetic population are initial sets of agents with detailed demographic and socioeconomic attributes, which allow execution of agent-based microsimulation.[5] Due to privacy reasons and data limitations and restrict observability of entire real population. Therefore, the population synthesis procedure is applied, which expands a small data sample of population by using auxiliary data, to generate a synthetic population as close as possible to the real population in its characteristics.

Examples of application

Chicago Social Interaction Model or chiSIM is an agent-based simulation of individuals and locations in Chicago along with their daily behavior. The population is modeled as a set of heterogeneous, interacting, adaptive agents. These agents are the population of all the residents of Chicago.[6]

In 2023, World Data Lab created a synthetic population for New York using microdata and summary statistics.[7] It was used to calculate the poverty levels among the neighborhoods for targeted social programs.

References

  1. Huynh . N . Namazi-Rad . Mohammad-Reza . Perez . P. . Berryman . M. . Chen . Q. . Barthelemy . J. . Generating a synthetic population in support of agent-based modeling of transportation in Sydney . Faculty of Engineering and Information Sciences - Papers: Part A . 1 January 2013 . 1357–1363 .
  2. Hörl . Sebastian . Balac . Milos . Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data . Transportation Research Part C . 2021 . 130 . 103291 . 10.1016/j.trc.2021.103291 . en. 20.500.11850/495494 . free .
  3. Xu . Zhujing . Glass . Kathryn . Lau . Colleen L. . Geard . Nicholas . Graves . Patricia . Clements . Archie . A synthetic population for modelling the dynamics of infectious disease transmission in American Samoa . Scientific Reports . 2017 . 7 . 1 . 16725 . 10.1038/s41598-017-17093-8 . 29196679 . 5711879 . 2017NatSR...716725X . 256907125 . en . 2045-2322.
  4. Mueller . Kirill . Axhausen . Kay W. . Hierarchical IPF: Generating a synthetic population for Switzerland . ERSA Conference Papers . September 2011 .
  5. Zhu . Yi . Ferreira . Joseph . Synthetic Population Generation at Disaggregated Spatial Scales for Land Use and Transportation Microsimulation . Transportation Research Record . January 2014 . 2429 . 1 . 168–177 . 10.3141/2429-18. 16819119 .
  6. Book: Macal . Charles M. . Collier . Nicholson T. . Ozik . Jonathan . Tatara . Eric R. . Murphy . John T. . 2018 Winter Simulation Conference (WSC) . Chisim: An Agent-Based Simulation Model of Social Interactions in a Large Urban Area . December 2018 . 810–820 . 10.1109/WSC.2018.8632409 . 978-1-5386-6572-5 . 59600666 . https://ieeexplore.ieee.org/document/8632409.
  7. News: Fighting poverty with synthetic data . 6 July 2023 . Brookings . 2023.

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